In the world of business intelligence, roles can vary significantly based on the size of the organization and the specific responsibilities assigned.
Let’s explore two scenarios to illustrate these differences:

Enterprise Ed works at a large corporation with a substantial IT team. He is working on a project to segment user data for the Marketing team. Ed notifies the business analyst (BA) about that he is ready the work on the new sprint, the BA documents the required logic and requests the changes from the data engineering team to update the source data in the database. Once the data is updated, he can continue working on the report.

Mike on the other hand manages the scope of the project, documents the new requirements, implements the logic in the database layer, and continues working on the report design; it's an iterative process.

Both Ed and Mike are Business Intelligence developers or have a similar title but their workflows differ significantly due to the size and structure of their teams.

I read about roles such as architect, data modeler, Power BI admin, and data visualization expert. However, I assume that most companies do not have these specialized roles, and instead, developers have different strengths and have multiple roles accordingly.

Power BI developer vs BI developer
The term Power BI developer suggests that the person is specialized in Power BI and is part of team of other specialized roles. Power BI is a quickly expanding universe, MS Fabric being the latest jump in complexity and required knowledge. If your position is named based on a BI tool, it can seem like that your knowledge is strictly limited to this tool.

Fabric

Now that Power BI became a part of Fabric, the differences between Ed and Mike can grow even faster, Ed might get a new colleague, a Fabric Analytics Engineer while Mike will study Fabric, take the DP-600 exam and incorporate Fabric into the Power BI development process. The analytics engineer role aims to bridge the gap between professionals who exclusively focus on Power BI and those in data engineering. In smaller teams the Power BI developer might become an analytics engineer or an analytics engineer will also create the reports.

I think at this point I am more of an analytics engineer but not because I started to use Fabric, Fabric is just another development environment to use for the same purpose, to create data sources, semantic models designed for specific analytics requirements in collaboration with business users. I definitely don't think that I am the data visualization business even if that is the end result most users see, but that is just the tip of the iceberg. In my mind, this is data visualization: Link.
https://careerfoundry.com/en/blog/data-analytics/analytics-engineer/ https://www.reddit.com/r/dataengineering/comments/1dieubi/data_engineer_vs_analytics_engineer_vs_data/

I wonder how do recruiters differentiate between the candidates to match the expectations of the future employers? At which point the term Power BI developer becomes inaccurate for people like Mike?

Recommended YouTube video in this topic from the PowerBI.tips podcast:
https://www.youtube.com/watch?v=KcbPsxy7TGI

Recommended article:
https://sqlserverbi.blog/2014/06/17/business-intelligence-roles-and-team-composition/

This image from Data Bear says it all.

I would love to include articles or posts from recruiters here, if you are a recruiter and you wrote a post in this topic, please send the link via LinkedIn and I'll add here.
Notes:
Recruiters are relying on the title of your current job and also from your tech stack to determine your position on the scale discussed above.